Evidence map›Paper›PMID 40684144›Full record

ArticleBMC public health2025

The dynamic equilibrium between population aging and the allocation of elderly medical care resources in the Yellow River Basin.

Long Hai, Yanghao Cui, Jiawei Zhang, Rui Wang

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Long HaiResearch Center on Social Work and Social Governance, Henan Normal University, Xinxiang, Henan, 453000, China.
Yanghao CuiResearch Center on Social Work and Social Governance, Henan Normal University, Xinxiang, Henan, 453000, China. 835170509@qq.com.
Jiawei ZhangResearch Center on Social Work and Social Governance, Henan Normal University, Xinxiang, Henan, 453000, China.
Rui WangResearch Center on Social Work and Social Governance, Henan Normal University, Xinxiang, Henan, 453000, China. 13069507290@163.com.

Funding

the Key Research and Development and Promotion Project of Henan Province 222400410009the Soft Science Research Program of Xinxiang RKX2024002
6 · The paper itself

Abstract

backgroundPopulation aging, as a global issue, has garnered significant attention worldwide. The rational allocation of elderly healthcare resources is critical to addressing this challenge. This study investigates the spatiotemporal evolution of population aging in the Yellow River Basin and evaluates the equilibrium between aging demographics and healthcare resource distribution.

methodsThis paper uses the Dagum Gini coefficient decomposition method to analyze regional differences and the dynamic evolution process of population aging in various provinces of the Yellow River Basin. The entropy method is employed to evaluate the elderly care and medical resource allocation in the provinces of the Yellow River Basin in 2020. By combining geographical concentration and the inconsistency index, the study investigates the matching relationship and distribution characteristics between aging levels and elderly care medical resource allocation.

resultsThe level of aging in the provinces of the Yellow River Basin has been deepening, showing a stepwise increase from west to east. The spatial imbalance in aging, as measured by the Dagum Gini coefficient, decreased from 0.126 in 2000 to 0.099 in 2020. However, the contribution rate of intra-regional differences rose from 23.096% to 40.487%, making it the primary source of disparity. The core provinces (Shandong, Henan, Sichuan) had comprehensive scores of 0.946, 0.846, and 0.793, respectively, while the peripheral provinces (Qinghai, Gansu, Ningxia) scored only 0.001, 0.226, and 0.012. A positive correlation was found between the elderly population and elderly healthcare resources, but 55.56% of the provinces exhibited either resource lag (RI > 1) or over-allocation (RI < 0.8), indicating a mismatch.

conclusionThe Yellow River Basin displays marked spatiotemporal heterogeneity in aging and resource distribution. Most provinces exhibit incoordinate matching relationships, characterized by insufficient supply or low utilization efficiency. Provincial authorities should implement region-specific policies, optimizing resource allocation and tailoring strategies to local demographic-geographic conditions.

Indexed as

AgingHealth Care RationingHealth Services for the AgedPopulation DynamicsResource AllocationAgedAged, 80 and overChinaFemaleHumansMaleRiversSpatio-Temporal AnalysisElderly medical resourcesPopulation agingSpatial matchingYellow River Basin

Identifiers

PMID40684144
PMCPMC12275301

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.